Surveillance of infectious diseases spreading on time-varying multiplex networks

Surveillance of infectious disease transmission is crucial for early detection and timely intervention. Existing studies mainly focus on static single-layer networks, primarily aiming to identify which types of nodes can provide early warning signals and accurate information on infections. Yet, real-world contact patterns are multiplex and time-varying, strongly shaping epidemic dynamics. Here, we propose a modeling framework for disease spread on time-varying multiplex networks and evaluate five node selection strategies: most connected, random, friends of random, most recent contacts, and most frequent contacts by using three metrics: early warning, peak timing, and peak ratio. These strategies are also tested across three scenarios with varying levels of structural information: Aggregated, Single-layer and Coupled networks. Simulation results show that the most connected strategy yields the best results across all metrics and scenarios, but it is costly and often impractical when full network information is unavailable. Importantly, our findings exhibit that the frequent-contact strategy on Coupled networks offers a practical alternative, achieving performance comparable to the most connected approach. Sensitivity analyses confirm the robustness of these findings. Our results highlight the importance of accounting for multiplexity and temporal dynamics in surveillance design and provide guidance for effective sentinel placement in epidemic monitoring.